Overview
- Presents a Chemical Optimization Algorithm for Fuzzy Controller Design
- The novel algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm
- Includes an application to a dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory
- Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (7 chapters)
Keywords
About this book
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions.
This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application.
Authors and Affiliations
Bibliographic Information
Book Title: Chemical Optimization Algorithm for Fuzzy Controller Design
Authors: Leslie Astudillo, Patricia Melin, Oscar Castillo
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-319-05245-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014
Softcover ISBN: 978-3-319-05244-1Published: 26 March 2014
eBook ISBN: 978-3-319-05245-8Published: 13 March 2014
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: VIII, 77
Number of Illustrations: 32 b/w illustrations
Topics: Computational Intelligence, Control and Systems Theory, Theoretical and Computational Chemistry, Robotics and Automation, Artificial Intelligence